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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Kannan, Deeba
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Electrical & Electronics Engineering

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Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon Kannan, Deeba; Balakrishnan, Amutha; Devi, K. Mekala; Singh, Nagendra; Kiruba, P. Angelin; Ramkumar, Ravindran; Karthikeyan, Dhandapani
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5432

Abstract

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.
Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks Kannan, Deeba; Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan; Venkatarajan, Shanmugasundaram; Mahajan, Rashima; Gunasekaran, Brindha; Murugamani, Pandi Maharajan; Dhandapani, Karthikeyan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2090-2100

Abstract

In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
Co-Authors Balakrishnan, Amutha Devi, K. Mekala Dhandapani, Karthikeyan Gunasekaran, Brindha Karthikeyan, Dhandapani Kiruba, P. Angelin Mahajan, Rashima Murugamani, Pandi Maharajan Ramkumar, Ravindran Singh, Nagendra Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan Venkatarajan, Shanmugasundaram